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AC和PA的SMC进一步分析

source("tianfengRwrappers.R")

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library(org.Hs.eg.db)

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library(msigdbr)
library(GSVA)
library(fgsea)

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library(UCell)

分离AC和PA的SMC亚群

```r
select.cells <- CellSelector(plot = DimPlot(ds2, reduction = \umap\)) #去除边角的离群细胞
ds2 <- subset(ds2, cell = select.cells)
# saveRDS(ds2,\ds2.rds\)
umapplot(ds2,split.by = \conditions\)
ds2 <- ds2 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.15)
umapplot(ds2, group.by = \seurat_clusters\,split.by = \conditions\)
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = \AC\)
ds2_PA <- subset(ds2, idents = \PA\)
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)
umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)

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## find markers

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```r
ds2_markers <- FindAllMarkers(ds2, logfc.threshold = 0.5, min.diff.pct = 0.2, only.pos = F)

Calculating cluster SMC1

Calculating cluster Fibromyocyte

Calculating cluster Pericyte

Calculating cluster Fibroblast

Calculating cluster SMC2

ds2_markers_pos <- ds2_markers[ds2_markers$avg_logFC>0, ]
ds2_markers_neg <- ds2_markers[ds2_markers$avg_logFC<0, ]

GO and KEGG

GSVA

exprmat <- get_data_table(ds2, highvar = T,type = "data")
clusterinfo <- ds2@meta.data[,c("orig.ident","Classification1")]
mbd <- msigdbr(species = "Homo sapiens", category = "C7") # C7 免疫
msigdbr_list <- split(x = mbd$gene_symbol, f = mbd$gs_name)

immo_res <- gsva(exprmat, msigdbr_list, kcdf="Gaussian",method = "gsva", parallel.sz = 6) #gsva 在server上运行
pheatmap(immo_res, show_rownames=1, show_colnames=0, 
         annotation_col=clusterinfo,fontsize_row=5, wiidth=8, height=6)#绘制热图

es <- data.frame(t(immo_res),stringsAsFactors=F)  #添加到单细胞矩阵中，可视化相关通路的在umap上聚集情况，可理解为一个通路即一个基因
dataset1 <- AddclusterinfoData(pbmc, es)
FeaturePlot(dataset1, features = "KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS", reduction = 'umap')

#GSEA

addmodulescore

geneset <- read.table("fibromyo")
dataset1 <- AddModuleScore(dataset1,features = geneset, name = 'fibromyo_score')
f("fibromyo_score1", dataset1, min.cutoff = 0)
dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')
f("V1fibromyo_score", dataset1)

SMC2

ds2

umapplot(ds2)
ds2_markers <- FindMarkers(ds2,ident.1 = "SMC2",min.diff.pct = 0.2,logfc.threshold = 0.5)
ds2_markers_pos <- ds2_markers[ds2_markers$avg_logFC>0, ]
ds2_markers_neg <- ds2_markers[ds2_markers$avg_logFC<0, ]

ds1

umapplot(ds1)

ds1_markers <- FindMarkers(ds1,ident.1 = "SMC2",min.diff.pct = 0.2,logfc.threshold = 0.5)

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ds1_markers_pos <- ds1_markers[ds1_markers$avg_logFC>0, ]
ds1_markers_neg <- ds1_markers[ds1_markers$avg_logFC<0, ]

gene_list <- rownames(ds1_markers_pos)
up_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(up_enrich.go, showCategory = 10)

cnetplot(up_enrich.go, showCategory = 10)

emapplot(up_enrich.go, showCategory = 10)

##down-regulated genes
gene_list <- rownames(ds1_markers_neg)
down_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(down_enrich.go, showCategory = 10)

cnetplot(down_enrich.go, showCategory = 10)

emapplot(down_enrich.go, showCategory = 10)

BMP通路相关

雷达图

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